Invasive Cervical Cancer Risk Among HIV-Infected Women
Bibliographic record
Abstract
OBJECTIVE: HIV infection and low CD4+ T-cell count are associated with an increased risk of persistent oncogenic human papillomavirus infection-the major risk factor for cervical cancer. Few reported prospective cohort studies have characterized the incidence of invasive cervical cancer (ICC) in HIV-infected women. METHODS: Data were obtained from HIV-infected and -uninfected female participants in the North American AIDS Cohort Collaboration on Research and Design with no history of ICC at enrollment. Participants were followed from study entry or January 1996 through ICC, loss to follow-up, or December 2010. The relationship of HIV infection and CD4+ T-cell count with risk of ICC was assessed using age-adjusted Poisson regression models and standardized incidence ratios. All cases were confirmed by cancer registry records and/or pathology reports. Cervical cytology screening history was assessed through medical record abstraction. RESULTS: A total of 13,690 HIV-infected and 12,021 HIV-uninfected women contributed 66,249 and 70,815 person-years of observation, respectively. Incident ICC was diagnosed in 17 HIV-infected and 4 HIV-uninfected women (incidence rate of 26 and 6 per 100,000 person-years, respectively). HIV-infected women with baseline CD4+ T-cells of ≥350, 200-349, and <200 cells per microliter had a 2.3, 3.0, and 7.7 times increase in ICC incidence, respectively, compared with HIV-uninfected women (P(trend) = 0.001). Of the 17 HIV-infected women, medical records for the 5 years before diagnosis showed that 6 had no documented screening, 5 had screening with low-grade or normal results, and 6 had high-grade results. CONCLUSIONS: This study found elevated incidence of ICC in HIV-infected compared with -uninfected women, and these rates increased with immunosuppression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".